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ENVS3035 Application of Bayesian Networks in Natural Resource Management

Later Year Course

Offered By School of Resources Environment & Society
Academic Career Undergraduate
Course Subject Environmental Science
Offered in Autumn Session, 2009
Unit Value 6 units
Course Description

Bayesian networks (BNs) are ideal models for natural resource management as they are able to represent complex natural systems, integrate different sources and types of information and investigate alternative management and system change scenarios. Increasingly, BNs are being used in Natural Resource Management (NRM) applications in Australia, including water and climate related issues. They also have a long history being applied in other fields, such as medicine and engineering. In this course we seek to provide a balance between theory and practice for developing and applying BNs within NRM. Existing BN models, built for NRM applications, will be used to illustrate theoretical concepts.

Key components of the course are insights into ongoing research being undertaken in iCAM.

Learning Outcomes

On satisfying the requirements of this course, students will have the knowledge and skills to:

1. Describe the theoretical concepts underpinning Bayesian networks and the participatory processes required for model building
2. Integrate qualitative and quantitative data and other model forms of evidence into Bayesian networks, and apply these to integration, risk, climate change and conservation assessments
3. Integrate Bayesian networks and Geographic Information Systems, and develop practical skills in building, evaluating and using Bayesian networks for natural resource management purposes
4. Analyse best practice in building and implementing Bayesian networks for natural resource management purposes

Indicative Assessment

Assessment will be based on:

  • 2000-word literature review (25%; LO 1)
  • Short presentation explaining the construction of a Bayesian network (15%; LO 2, 3, 4)
  • 2000-word final report on building a specific Bayesian network (60%; LO 2, 3, 4)
Workload

Autumn session (20 April -1 May 2009) 65 hours of contact taught as a two-week block course, comprising lectures and practical components.

Areas of Interest Resource Management and Environmental Science
Preliminary Reading

Cain JD. 2001. Planning improvements in natural resources management: Guidelines for using Bayesian Networks to support the planning and management of development programmes in the water sector and beyond. Wallingford, UK: Centre for Ecology and Hydrology. Available at http://www.norsys.com/resources.htm

Science Group C
Academic Contact Dr Carmel Pollino

The information published on the Study at ANU 2009 website applies to the 2009 academic year only. All information provided on this website replaces the information contained in the Study at ANU 2008 website.

Updated:   13 Nov 2015 / Responsible Officer:   The Registrar / Page Contact:   Student Business Solutions